When and how to use PDC in drug development

When and how to use PDC in drug development

by Imagen Therapeutics|February 15, 2022 at 12:13 PM

So, you’ve used conventional methodologies to identify a target, designed a series of candidate drugs and validated a biomarker for its expression by a particular tumour type. What’s next? If you are looking to validate your new agent efficacy and potency you are probably considering what preclinical model to select. The poor translatability of standard cancer cell lines and the time and cost associated with in vivo models cause you some concerns and you are probably wondering whether there is any viable alternative. If this sounds familiar, this blog post might provide some insights. Here we discuss why we believe patient-derived cell (PDC) models represent the ideal model of choice for preclinical drug development.

First and foremost, what are PDC?

PDC stands for Patient-Derived Cell models and as the name implies these models are developed from a fresh tumour biopsy obtained from patients across the world. The cancer cells reach our labs and are then separated and grown in a specifically formulated medium until there are enough cells for testing.

This process is fast and highly efficient, with a close to 90% success rate for model establishment and has allowed us to generate a large biobank of low-passage PDC models sourced worldwide. A large biobank of PDC models allows us to mimic in our laboratory the heterogeneity observed in the cancer patient population – therefore simulating a clinical trial in the lab – and represents a unique asset for screening new drugs, assessing potency and efficacy, and to stratify patients before moving to the clinic.

Once in our biobank our PDC models undergo extensive characterisation to ensure they remain faithful to the original tumour and to better understand their gene expression profile. They are also tested for response to standard of care (SOC) agents approved for the specific tumour type and all our data including

  • Patient clinical and treatment history
  • Phenotypic drug response data to ~60 approved clinical drugs (including SOC)
  • NGS (WES and RNAseq)
  • Post-collection treatment responses

These data are then stored within an online database called predictDb.

By getting access to our database, you can select your model of interest and enrol them in our predictTx in vitro pharmacology platform.

At what stage of drug development should I incorporate PDC screening?

PDC’s can help you make informed decisions at every stage of the drug development process from target validation or lead optimisation to modelling clinical trials. They can help you answer questions such as:

  • Is my molecular target hypothesis robust?
  • What proportion of my patient population presents a specific biomarker, and do they all respond?
  • Does the degree of response to treatment correlate with the biomarker level of expression?
  • Can I extend the potential indication to other tumour types?
  • Are there other pathways or biomarkers against which my candidate is effective?

The figure below illustrates typical use-case scenarios currently in use with our clients at the preclinical phase. The applications to translational and clinical development will be discussed in our next blog post.

preclinical-phase-scenario-used-1.jpg

Can PDC mirror patient response in the clinic?

The short answer is yes! Our data show that PDC can successfully predict patient response (PPV) to treatment in 89% of the cases and in almost all cases they can predict lack of response (NPV).

PDC-mirror-patient-response-1.jpg

Summary

In summary PDC models are innovative, patient-relevant in vitro models that can help you make informed decisions on whether to move your lead agent to clinical candidate. PDC can be rapidly developed directly from patient tumour biopsies, they can be frozen down and then thawed for expansion for preclinical drug screening services. PDC’s can be used at multiple stages in drug development and have been shown to be able to predict patient response in the clinic effectively thus offering a more cost-effective solution compared to an in vivo study.